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1. caltechdata_api – v1.10.1 - Added documentation and expanded cli options

   https://doi.org/10.22002/k51v8-cmz08  

   Morrell, Thomas E; Bhattarai, Rohan; Won, Elizabeth; Abakah, Alexander A; Nagi, Kshemaahna (July 2025, CaltechDATA)

   CLI Documentation for Users an expanded CLI file options CLI Documentation for Users by @Kshemaahna in https://github.com/caltechlibrary/caltechdata_api/pull/66 Expanded CLI file options by @Kshemaahna and @tmorrell New Contributors @Kshemaahna made their first contribution in https://github.com/caltechlibrary/caltechdata_api/pull/66 Full Changelog: https://github.com/caltechlibrary/caltechdata_api/compare/v1.10.0...v1.10.1 

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2. SNIKT: sequence-independent adapter identification and removal in long-read shotgun sequencing data

   https://doi.org/10.1093/bioinformatics/btac389  

   Ranjan, Piyush; Brown, Christopher A; Erb-Downward, John R; Dickson, Robert P (June 2022, Bioinformatics)

   Alkan, Can (Ed.)

   Abstract Summary Here, we introduce SNIKT, a command-line tool for sequence-independent visual confirmation and input-assisted removal of adapter contamination in whole-genome shotgun or metagenomic shotgun long-read sequencing DNA or RNA data. Availability and Implementation SNIKT is implemented in R and is compatible with Unix-like platforms. The source code, along with documentation, is freely available under an MIT license at https://github.com/piyuranjan/SNIKT. Supplementary information Supplementary data are available at Bioinformatics online. 

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3. CONSTAX2: improved taxonomic classification of environmental DNA markers

   https://doi.org/10.1093/bioinformatics/btab347  

   Liber, Julian A; Bonito, Gregory; Benucci, Gian Maria (May 2021, Bioinformatics)

   Marschall, Tobias (Ed.)

   Abstract Summary CONSTAX—the CONSensus TAXonomy classifier—was developed for accurate and reproducible taxonomic annotation of fungal rDNA amplicon sequences and is based upon a consensus approach of RDP, SINTAX and UTAX algorithms. CONSTAX2 extends these features to classify prokaryotes as well as eukaryotes and incorporates BLAST-based classifiers to reduce classification errors. Additionally, CONSTAX2 implements a conda-installable command-line tool with improved classification metrics, faster training, multithreading support, capacity to incorporate external taxonomic databases and new isolate matching and high-level taxonomy tools, replete with documentation and example tutorials. Availability and implementation CONSTAX2 is available at https://github.com/liberjul/CONSTAXv2, and is packaged for Linux and MacOS from Bioconda with use under the MIT License. A tutorial and documentation are available at https://constax.readthedocs.io/en/latest/. Data and scripts associated with the manuscript are available at https://github.com/liberjul/CONSTAXv2_ms_code. Supplementary information Supplementary data are available at Bioinformatics online. 

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4. dadi-cli: Automated and distributed population genetic model inference from allele frequency spectra

   https://doi.org/10.1101/2023.06.15.545182  

   Huang, Xin; Struck, Travis J; Davey, Sean W; Gutenkunst, Ryan N (June 2023, bioRxiv)

   Abstract Summarydadi is a popular software package for inferring models of demographic history and natural selection from population genomic data. But using dadi requires Python scripting and manual parallelization of optimization jobs. We developed dadi-cli to simplify dadi usage and also enable straighforward distributed computing. Availability and Implementationdadi-cli is implemented in Python and released under the Apache License 2.0. The source code is available athttps://github.com/xin-huang/dadi-cli. dadi-cli can be installed via PyPI and conda, and is also available through Cacao on Jetstream2https://cacao.jetstream-cloud.org/. 

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5. kegg_pull: a software package for the RESTful access and pulling from the Kyoto Encyclopedia of Gene and Genomes

   https://doi.org/10.1186/s12859-023-05208-0  

   Huckvale, Erik; Moseley, Hunter N. (December 2023, BMC Bioinformatics)

   Abstract Background The Kyoto Encyclopedia of Genes and Genomes (KEGG) provides organized genomic, biomolecular, and metabolic information and knowledge that is reasonably current and highly useful for a wide range of analyses and modeling. KEGG follows the principles of data stewardship to be findable, accessible, interoperable, and reusable (FAIR) by providing RESTful access to their database entries via their web-accessible KEGG API. However, the overall FAIRness of KEGG is often limited by the library and software package support available in a given programming language. While R library support for KEGG is fairly strong, Python library support has been lacking. Moreover, there is no software that provides extensive command line level support for KEGG access and utilization. Results We present kegg_pull, a package implemented in the Python programming language that provides better KEGG access and utilization functionality than previous libraries and software packages. Not only does kegg_pull include an application programming interface (API) for Python programming, it also provides a command line interface (CLI) that enables utilization of KEGG for a wide range of shell scripting and data analysis pipeline use-cases. As kegg_pull’s name implies, both the API and CLI provide versatile options for pulling (downloading and saving) an arbitrary (user defined) number of database entries from the KEGG API. Moreover, this functionality is implemented to efficiently utilize multiple central processing unit cores as demonstrated in several performance tests. Many options are provided to optimize fault-tolerant performance across a single or multiple processes, with recommendations provided based on extensive testing and practical network considerations. Conclusions The new kegg_pull package enables new flexible KEGG retrieval use cases not available in previous software packages. The most notable new feature that kegg_pull provides is its ability to robustly pull an arbitrary number of KEGG entries with a single API method or CLI command, including pulling an entire KEGG database. We provide recommendations to users for the most effective use of kegg_pull according to their network and computational circumstances. 

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